Open Interpreter is an open-source tool that allows developers to work with large language models (LLMs) on their local machine. It can be used via a terminal interface and supports popular programming languages, including Python, JavaScript, and Bash.
With Open Interpreter, users can perform various tasks, such as conducting data analysis, controlling web browsers for research purposes, generating and editing photos, videos, and PDFs, as well as managing and analyzing large datasets.
One of the most important features of Open Interpreter is its ability to control a PC’s graphical user interface (GUI), allowing for direct interaction with the desktop environment. Additionally, it now includes vision capabilities, enabling it to analyze and interpret images.
Open Interpreter is open sourced under the AGPL-3.0 license. It has over 50K stars on GitHub and has over 100 contributors. Get Open Interpreter from the official GitHub repo
This is part of a series of articles about code interpreter (content to come).
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OpenAI’s Code Interpreter is a tool integrated with GPT-3.5 and GPT-4, which can perform various computational tasks using natural language. However, it operates in a hosted, closed-source environment with several restrictions, such as limited access to pre-installed packages and the absence of internet connectivity. Additionally, it has a runtime limit of 120 seconds and a file upload cap of 100MB, which can be limiting for extensive or complex tasks.
Open Interpreter offers a more flexible solution by running directly on your local machine. This local execution provides full access to the internet and allows the use of any package or library needed for your projects. With no restrictions on runtime or file size, Open Interpreter is well-suited for handling large datasets and lengthy computations. Its open-source nature ensures that you have complete control over the tool and your data. It also supports multiple large language models (LLMs) beyond those offered by OpenAI.
Which LLMs Can You Use with Open Interpreter?
Open Interpreter uses LiteLLM to connect the terminal interface or development environment to hosted language models. When running open interpreter, users can determine what LLM model to run, like this:
You can see a full list of supported LLMs here. The following LLM providers are supported:
Provider
Special Comments
OpenAI
Supports Chat + Embedding calls
OpenAI (Text Completion)
Supports text completion models
OpenAI-Compatible Endpoints
Allows models hosted behind an OpenAI proxy
Azure OpenAI
API Keys, Params
Azure AI Studio
Supports all models on Azure AI Studio
VertexAI
Supports Anthropic, Gemini, Model Garden
PaLM API – Google
Gemini – Google AI Studio
Anthropic
Supports all Anthropic models
AWS Sagemaker
Supports all Sagemaker Huggingface Jumpstart Models
AWS Bedrock
Supports Anthropic, Amazon Titan, A121 LLMs
Mistral AI API
Codestral API
Available in select code-completion plugins, direct query support
Cohere
API KEYS
Anyscale
Huggingface
Supports various Huggingface models
Databricks
Supports all models on Databricks
IBM watsonx.ai
Supports all IBM watsonx.ai foundational models and embeddings
Predibase
Supports all models on Predibase
Nvidia NIM
Volcano Engine (Volcengine)
Triton Inference Server
Supports Embedding Models on Triton Inference Servers
Ollama
Supports all models from Ollama
Perplexity AI (pplx-api)
Groq
Deepseek
Fireworks AI
Clarifai
Supports Anthropic, OpenAI, Mistral, Llama, and Gemini LLMs
VLLM
Supports all models on VLLM
Xinference (Xorbits Inference)
Cloudflare Workers AI
DeepInfra
AI21
Supports j2-light, j2-mid, and j2-ultra from AI21
NLP Cloud
Supports all LLMs on NLP Cloud
Replicate
Supports all models on Replicate
Together AI
Supports all models on Together AI
Voyage AI
Aleph Alpha
Supports all models from Aleph Alpha
Baseten
Supports any Text-Gen-Interface models on Baseten
OpenRouter
Supports all the text/chat/vision models from OpenRouter
Custom API Server (OpenAI Format)
Allows custom endpoint in OpenAI ChatCompletion format
Petals
Tutorial: Getting Started with Open Interpreter
Setup
To get started with Open Interpreter, you first need to install it via pip. Make sure you have Python 3.10 or 3.11 installed on your machine. You can check your Python version by running:
python --version
It is recommended to use a virtual environment to manage your dependencies. Once your environment is set up, you can install Open Interpreter with the following command:
pip install open-interpreter
Start a Chat
Open Interpreter operates similarly to ChatGPT but runs locally on your machine. To start a new interactive chat, open the terminal and run:
interpreter
In Python, Open Interpreter retains the conversation history within a session. To start a fresh chat, you can reset the conversation history:
interpreter.messages = []
Save and Restore Chats
Open Interpreter allows you to save and restore conversations, ensuring you can pick up where you left off. In the terminal, conversations are saved in the<your application directory>/Open Interpreter/conversations/folder. To resume a saved chat, use the following command and navigate with your arrow keys to select a conversation:
interpreter --conversations
In Python, you can save the chat messages to a list and restore them later:
messages = interpreter.chat("Create a Python function…")
To reset the interpreter:
interpreter.messages = []
To resume the chat from the messages lists, use:
interpreter.messages = messages
Customize System Messages
You can customize the system message to modify permissions or provide additional context. In the terminal, this is done by editing the configuration file as described in the documentation. In Python, you can adjust the system message directly:
interpreter.system_message += """ Run shell commands with -y so the user doesn't have to confirm them. """ print(interpreter.system_message)
Change the Language Model
Open Interpreter uses LiteLLM to interface with different language models. You can switch models by setting the model parameter. In the terminal, use this syntax. For a full list of supported models, see Which LLMs Can You Use above.
interpreter --model gpt-4-turbo
In Python, you can set the model directly on the interpreter object:
interpreter.llm.model = "gpt-4-turbo"
Run Code Using Open Interpreter
You can run code directly through Open Interpreter. The computing environment is separate from the interpreter’s core, allowing independent execution. Here’s how to run a simple Python command:
from interpreter import interpreter interpreter.computer.run("python", "print('Welcome!')")
You can also prepare the environment by defining functions, setting variables, or logging into services before executing code:
interpreter.computer.run("python", "import replicatenreplicate.api_key='...'") interpreter.custom_instructions = "Replicate is already imported." interpreter.chat("Please create a new image with Replicate...")
